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NCT Number: NCT06463756

AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma

This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.

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Key information

Conditions

Laryngeal Carcinoma Head and Neck Neoplasms Laryngeal Diseases Laryngeal Neoplasms Neoplasms Neoplasms by Site Otorhinolaryngologic Diseases Otorhinolaryngologic Neoplasms Respiratory Tract Diseases Respiratory Tract Neoplasms Thyroid Cartilage

Age range

18 year–81 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Chongqing Medical University

Chongqing, China

Location status: Recruiting

Location contact

Peng juan

CONTACT

[email protected]

+86 189 8328 0171

About this study

Laryngeal squamous cell carcinoma (LSCC), as one of the most common head and neck tumors, is the eighth leading cause of cancer-associated death worldwide. The treatment decisions has a profound impact on both tumor control and functional prognosis of LSCC patients. And these decisions are primarily based on tumor staging, with the invasion of the thyroid cartilage serving as a crucial determinant. Consequently, the presence of thyroid cartilage invasion indicates an advanced stage (T3 or T4) diagnosis for the LSCC patients. For patients without thyroid cartilage invasion, partial laryngectomy may be considered to preserve laryngeal function. However, for patients with advanced laryngeal carcinoma and thyroid cartilage invasion extending beyond the larynx, total laryngectomy is often necessary to completely remove the tumor and extend survival time. Therefore, accurate assessment of thyroid cartilage invasion is vital for treatment decision-making and prognosis evaluation for LSCC patients.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Availability of complete clinical data
  • Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
  • CT examination performed within 2 weeks before surgery

Exclusion criteria

  • Patients who received preoperative chemotherapy or radiation therapy
  • CT images with significant artifacts
  • Patients with tumor recurrence

Treatment and study plan

AI

Other

Radiomics extracts quantitative information from medical images to generate high-dimensional feature vectors for analysis. It aims to provide insights into disease processes and improve diagnosis.

Deep learning utilizes neural networks with multiple layers to learn complex patterns from data. In medical imaging, it enables accurate and efficient analysis for disease detection and diagnosis.

Other names: radiomics, deep learning

Primary outcomes

  1. Area under the curve, AUC

    Time frame: Through study completion, an average of 6 months

    Area under the curve(AUC) is a metric widely used in machine learning and medical research to evaluate the performance of models in binary classification problems. It reflects the ability of a model to identify true positives (True Positives) while avoiding falsely classifying negative examples as positive (False Positives).

Secondary outcomes

  1. Disease-Free-Survival, DFS

    Time frame: The date of surgery and the occurrence of events such as disease progression, the date of the last follow-up, or death from any cause, and the follow-up time was at least 3 years

    Disease-Free Survival (DFS) refers to the time from the start of randomization (usually the starting point of a clinical trial) to the recurrence of the disease or death of the patient due to disease progression. DFS is an important clinical and statistical indicator used to evaluate the long-term effects of cancer treatment.

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Chongqing Medical University

Other

Collaborators

  • Nankai University

Registry information

Official study title

CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Jun 18, 2024
Registry last updated
Aug 22, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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